This invention is concerned with the recognition of types of physical body parts like faces, hands, legs or any other body parts. These body part types have to be detected in static or moving pictures irrespective of whether it is known that the required part is present or not. When such a body part is detected, its exact position (its coordinates) should be indicated in the image and its size in terms of the measuring system used should also be made available. The procedure must be automatic.
In recent years the techniques for face recognition have been based on the typical gray scale intensity of face images. This detection procedure as applied to static or moving images is based on a gray scale picture, but if this term is used below, its meaning may include other intensities like color pictures or extracts from color pictures, as well as any other type of pictorial information which involves intensity variations. If the term “gray scale value” is used below, it should be understood in this general sense.
One type of recognition procedure endeavors to detect facial features like eyes, nose or mouth independently and then determine their position. The individual localities are subsequently used to find the actual face in the image according to specified rules or on the basis of statistical models.
The evaluation of information about edge directions and edge clarity has been proposed in the technical literature, see the article by Donahue/Rokhlin on information about edge directions: “On the use of level curves in image analysis”, Image Understanding, Vol. 57 Nr 2, 1993, pages 185 to 203, especially Paragraphs 2 and 3 dealing with tangent vector calculation, and FIG. 2 in which the vector representation is illustrated. Elsewhere an operator is proposed which would be able to establish edges in digital images, compare Hueckel: “An operator which locates edges in digital pictures”, J. Assoc. Comput., March 1971, Vol. 18, pages 113 to 125. For the purpose of face recognition edge information (also described as information about borderlines) of Maio/Maltoni has been employed, see “Fast face location in complex backgrounds”, Face recognition, from theory to applications, NATO ASI Series F: Computer and Systems Sciences, Vol. 163, 1998, pages 568 to 577, as well as a later publication of the same authors in Pattern Recognition, Vol. 33, 2000, pages 1525 to 1539: “Real-time face location on gray-scale static images”.
In later publications gray scale images are shown in the respective FIG. 2 which have been converted to edge direction images in terms of vector lengths and consistency, called direction reliability. In those cases the vector direction represents the tangent to the edge of the image, and the length of the vector, called significance, comprises the sum of the contrast values in the sense of edge thickness or edge clarity. In addition, “consistency” is used and explained as direction reliability.
The evaluation of numerous pieces of information about direction, reliability, and clarity is complex, and requires a great deal of computing power. Even modern computers cannot provide sufficient computing power, and small computers cannot be used.